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ESTIMATING DEMAND IN EMERGING MARKETS FOR KODAK EXPRESS

David M. Currie and Ilan Alon wrote this case solely to provide material for class discussion. The authors do not intend to illustrate either effective or ineffective handling of a managerial situation. The authors may have disguised certain names and other identifying information to protect confidentiality. Richard Ivey School of Business Foundation prohibits any form of reproduction, storage or transmission without its written permission. Reproduction of this material is not covered under authorization by any reproduction rights organization. To order copies or request permission to reproduce materials, contact Ivey Publishing, Richard Ivey School of Business Foundation, The University of Western Ontario, London, Ontario, Canada, N6A 3K7; phone (519) 661-3208; fax (519) 661-3882; e-mail cases@ivey.uwo.ca. Copyright © 2011, Richard Ivey School of Business Foundation Version: 2011-07-08

Anna Johnson gazed at the information she had accumulated on various countries and wondered how she could use it to estimate the demand for Kodak Express (KE) outlets. She had learned from the Kodak market research department that demand for KE outlets depended on household income. To support one Kodak Express outlet, one of the following was needed: one million households with annual incomes equal to or exceeding the equivalent of US$15,000, two million households earning the equivalent of between US$10,000 and US$14,999, four million households earning the equivalent of between US$5,000 and US$9,999 or 10 million households with incomes less than the equivalent of US$5,000 (see Exhibit 1). According to the market research department, these averages seemed to apply throughout the world, when international dollars (purchasing power parity adjusted) were used as a benchmark. Unfortunately, the statistics Johnson was able to find did not tell her the household income in U.S. dollarequivalents in various countries or how many households in each country fell into a specific income bracket. She would need to complete a series of intermediate calculations to transform the original macroeconomic data to information that was useful for estimating the demand for Kodak Express outlets. Then, on the basis of market demand, Kodak would be able to use this information to decide how to allocate its investments across the various emerging markets. The purpose of Johnson’s calculations was to identify the markets with the most potential. The commitment to open KE outlets in a particular country was a significant investment that needed to have a promising return. Further, determining the markets with the most potential was in line with Kodak’s philosophy of “investing where you sell the most.” Johnson sat in front of her computer, attempting to develop a model that would help her to estimate the market demand for KE outlets. She picked up a pencil and paper and began to sketch the process she would follow to use the data at her disposal to determine demand for KE outlets. Once she determined the process, she would prepare a spreadsheet model, plug in the data for a country and see whether the result was reasonable.

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KODAK’S GLOBAL STRATEGY

The manufacture and distribution of photography items had been the major focus of Eastman Kodak Corporation since George Eastman commercialized personal cameras using roll film in 1888.1 The next year, the company became international when it extended distribution of products outside the United States. In 1900, Kodak introduced the first Brownie camera, the company’s effort to make photography available to a mass market. By 2002, the company’s products were available in more than 150 countries. However, the company was faced with increased competition from two fronts: Japan’s Fuji Photo Film Co. produced and marketed many of the same photography products as Kodak, and Kodak had been slow to respond to the emergence of digital photography.2 Worldwide revenues for Kodak’s products exceeded US$5.5 billion in 2010, a decrease of US$800 million compared with 2009 and a decrease of US$1.6 billion compared with 2008. The decline was partially due to the global economic slowdown, particularly in Europe, Middle East and Africa: revenues from these regions had decreased by more than US$1 billion in the previous two years.3 To offset the decline in sales volume in Europe, Middle East and Africa, Kodak’s strategy was to expand sales into emerging markets such as India and China. Because of China’s enormous population and its citizens’ cultural affinity for taking pictures, China was one of the cornerstones of Kodak’s emerging market strategy. Even expanding the market to just half of China’s population would add the equivalent of another United States or Japan to the world photographic market.4
KODAK EXPRESS OUTLETS

Despite Kodak Express outlets being independently owned, they were contractually obliged to buy and display exclusively Kodak products, and they utilized Kodak’s store specifications and the company’s brand elements. The outlets provided three benefits to Kodak: 1. A front-line retailing presence 2. Wide distribution of Kodak products, services and brand name 3. A strategic asset for Kodak for market development The company was making an effort to have a more extensive distribution of its Kodak Express outlets throughout the world. Through these outlets, Kodak planned to launch “grass-root marketing development programs.”5

1 Kodak, “Building the Foundation,” http://kodak.com/US/en/corp/kodakHistory/buildingTheFoundation.shtml, accessed January 26, 2004. 2 Daniel Gross, “Photo Finished: Why Eastman Kodak Deserves to Lose Its Dow Jones Industrial Average Membership,” Slate, January 6, 2004, http://slate.msn.com/id/2093512/, accessed January 26, 2004. 3 Kodak 2010 Annual Report; www.envisionreports.com/EK/2011/22103MA11E/38495aae46f94783a4829c3e66124a12/Kodak_AR_10k_Secured_3-2811.pdf, accessed June 16, 2011. 4 David Swift, “Remarks of David Swift, Chairman & President, Greater China Region, Eastman Kodak Company,” Goldman Sachs 21st Century China Conference, September 1999, pp.1-8. 5 Ilan Alon, “Interview: International Franchising with Kodak in China,” Thunderbird International Business Review, November/December 2001, pp. 737-754.

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DATA AVAILABILITY

Johnson focused on four sets of data that were readily available from reliable sources for a variety of countries: population, purchasing power, income distribution and average household size. The first data set, population, seemed straightforward. Any analysis of a country’s purchasing habits would begin with consumers, whether on an individual (per capita) basis or a household basis. Population estimates for 2010 are shown in Exhibit 2. More recent statistics were difficult to locate because most countries conducted a census only once every 10 years. Between censuses, all population statistics were estimates. Determining the dollar-equivalent level of income from one country to another was a more challenging task. Many statistics comparing one country with another merely converted data into U.S. dollars using an average exchange rate for the year. Although this method was useful for some purposes, it was potentially misleading when used for consumption patterns because it ignored the cost of living from one country to another. For example, a family earning RMB65,000 in China earned the equivalent of approximately US$10,000 using an exchange rate conversion of RMB6.5 RMB per U.S. dollar.6 But RMB65,000 in China purchased much more than US$10,000 purchased in the United States; thus, the family in China would be considered much better off by Chinese standards, and their consumption patterns might be closer to a family in the United States earning US$48,000. To account for this difference, economists frequently standardized data for differences in purchasing power, called purchasing power parity (PPP). Johnson was able to find gross domestic product (GDP) per capita in U.S. dollars using PPP for the countries in which she was interested (see Exhibit 3). A related problem was that GDP was not the same as national income, and Johnson needed to know a household’s income. After some research, she discovered that because the difference usually was not significant, economists frequently used GDP as a proxy for national income. Therefore, the GDP per capita for these countries could serve as a substitute for income per capita. That meant that the average person in India earned the equivalent of US$3,339 annually on a PPP basis in 2010. Income distribution was another important issue because if more people earned low incomes in a country, they wouldn’t be able to support as many KE outlets. Johnson needed to determine how many households corresponded to different income levels for any country. Data on income distribution are shown in Exhibit 4. For any country, population was divided into equal portions called quintiles (fifths), and each quintile showed the share of national income accruing to that quintile. For example, in India the bottom 20 per cent of the population accounted for 8.1 per cent of national income, and the top 20 per cent of the population accounted for 45.3 per cent of national income. Because each quintile represented 20 per cent of the population, about 243 million people (20 per cent of 1,216 million people) earned only 8.1 per cent of the country’s total income. At the other extreme, 243.2 million people earned 45.3 per cent of the country’s total income. If incomes were distributed evenly in a country, each quintile would account for 20 per cent of the national income (see Exhibit 4). The average size of a household would help to determine the number of households in a country and, thus, the number of households in each of the quintiles. Data for the average size of household shown as the number of persons per household are shown in Exhibit 5. Dividing the population of a quintile by the number of people per household would yield an estimate for the number of households in the country. Of course, the assumption was that the number of people per household did not change with income. Johnson realized, however, that average household size depended on both cultural and economic factors. In some countries, the custom was for an extended family (parents, children and grandparents) to live in the same
6

Exchange rate on April 18, 2011, http://finance.yahoo.com/currency-converter/, accessed June 16, 2011.

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household. In most countries, household size varied according to the level of income in the household because families earning higher incomes tended to have fewer children. Household size also varied between urban and rural areas: urban households tended to be smaller. For this analysis, Johnson would need to assume that the average household size applied throughout the country, simply because no reliable statistics were available on the differences between sizes of households for all the countries she wished to examine.
CALCULATING POTENTIAL DEMAND

As Johnson thought more about her task, she realized that she faced a two-step problem. First, she would need to calculate the household income in U.S. dollars for each quintile of the population. Only by doing this step would she then be able to separate households into each of the income brackets. This difference was important because each category was able to support a different number of KE outlets, as Johnson had learned from Kodak’s market research department. The second step would be to calculate the potential demand for KE outlets once she knew the number of households in each spending category. To attain this number, Johnson knew that she would need to complete several interim steps. Using her available data, she could make some initial calculations: she could determine the PPP GDP for the entire population, the population per quintile and the number of households per quintile. Using the population’s GDP and each country’s income distribution, she could then calculate the income per quintile. Dividing the income per quintile by the population per quintile, she knew she would derive the individual income per quintile, from which she could easily conclude the household income per quintile. Then, using at the household income per quintile, she would be able to determine how many households fell into each of the categories in Exhibit 1. Knowing how many households fell in each category, she could then determine the potential demand for KE outlets in a specific market. Her goal was to build a model that would enable her to evaluate each of the countries in Exhibit 2. She would test the model using the data for one country. If it worked, the computer would then do most or all of the subsequent calculations. Once the model was complete, Johnson would use it both to predict demand for Kodak Express outlets in selected emerging markets and to make recommendations to Kodak management regarding market entry and resource allocations in these countries. Finally, Johnson knew that such a significant investment should take not only today’s demand into account. To determine the most attractive markets, she would also need to determine whether those markets would still be attractive in the future. Therefore, she decided to also calculate, in addition to the 2010 calculations, each country’s demand for the year 2016. She was able to gather projected data on both PPP GDP per capita and populations for 2016 (see Exhibits 6 and 7); however, she was unable to find reliable forecasts for income distribution and average household sizes. She therefore made the naive assumption that these last two variables, income distribution and average household size, would not change significantly. What is the potential demand for KE outlets in the various emerging markets in 2010? What would be the demand by 2016? Given the assumptions, which markets will be the top candidates for investment?
We wish to thank Christopher Gassner for the collection of data and the development of new spreadsheets.

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Exhibit 1 NUMBER OF HOUSHOLDS AND INCOME LEVELS NEEDED TO SUPPORT ONE KODAK EXPRESS OUTLET

Income ≥$15,000 $10,000—$14,999 $5,000—$9,999 $0—$4,999
Source: Company estimates/assumptions.

# of Households 1 million 2 million 4 million 10 million

Exhibit 2 POPULATION OF SELECTED COUNTRIES, 2010

Country United States Bangladesh Brazil Cambodia China India Indonesia Laos Malaysia Nigeria Pakistan Russia South Africa Thailand Vietnam
Source: International Monetary Fund, 2010.

Population (in millions) 310 164 193 14 1,341 1,216 234 6 28 156 167 140 50 64 88

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Exhibit 3 GROSS DOMESTIC PRODUCT PURCHASING POWER PARITY PER CAPITA FOR SELECTED COUNTRIES, 2010 Gross Domestic Product Purchasing Power Parity per Capita (in US$) 47,284 1,572 11,239 2,112 7,519 3,339 4,394 2,436 14,670 2,422 2,791 15,837 10,498 9,187 3,134

Country United States Bangladesh Brazil Cambodia China India Indonesia Laos Malaysia Nigeria Pakistan Russia South Africa Thailand Vietnam
Source: International Monetary Fund, 2010.

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Exhibit 4 DISTRIBUTION OF INCOME FOR SELECTED COUNTRIES — VARIOUS DATES (AS A PERCENTAGE OF INCOME)

Country Brazil 58.7 19.6 11.8 6.9 3 6.5 5.7 8.1 7.4 8.5 6.4 5.1 9.1 9.7 9.8 11.3 11 12.3 10.8 9.7 12.8 12.9 14.7 14.9 14.9 16.2 15.8 14.7 16.3 18.9 22 20.4 21.3 21.6 22.8 21.9 21.3 20.7 13.9 9.6 5.6 52 47.8 45.3 45.5 41.4 44.4 48.6 40.5 50.2 Cambodia China India Indonesia Laos Malaysia Nigeria Pakistan Russia South Africa 62.7 18.8 9.9 5.6 3.1 Thailand 49.4 21.3 14.1 9.4 5.9 Vietnam 45.4 21.6 15.2 10.8 7.1

Quintile

United States

Bangladesh

Upper

45.8

40.8

Upper Middle

22.4

21.1

Middle

15.7

16.1

Lower Middle

10.7

12.6

Lower

5.4

9.4

Source: World Bank, World Development Indicators, various dates.

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Exhibit 5 AVERAGE HOUSELHOLD SIZE FOR SELECTED COUNTRIES, VARIOUS DATES Average Household Size (in persons) 2.6 6.0 3.6 3.2 3.4 5.3 3.4 5.2 4.4 4.9 7.2 2.7 3.7 3.5 4.4

Country United States Bangladesh Brazil Cambodia China India Indonesia Laos Malaysia Nigeria Pakistan Russia South Africa Thailand Vietnam
Source: Euromonitor International, 2010.

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Exhibit 6 FORECASTED POPULATION FOR SELECTED COUNTRIES FOR 2016 Population Country United States Bangladesh Brazil Cambodia China India Indonesia Laos Malaysia Nigeria Pakistan Russia South Africa Thailand Vietnam
Source: International Monetary Fund, 2011.

(in millions) 328 178 199 15 1,382 1,316 253 7 31 183 183 137 53 66 95

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Exhibit 7 FORECASTED GROSS DOMESTIC PRODUCT PURCHASING POWER PARITY PER CAPITA FOR SELECTED COUNTRIES FOR 2016 Gross Domestic Product Purchasing Power Parity per Capita (in US$) 57,320 2,340 15,193 3,183 13,729 5,398 6,556 3,675 19,541 3,242 3,678 22,717 13,607 12,681 4,803

Country United States Bangladesh Brazil Cambodia China India Indonesia Laos Malaysia Nigeria Pakistan Russia South Africa Thailand Vietnam
Source: International Monetary Fund, 2011.